Home  /  Books & Reference Apps  / Deep Learning for computer vision offline Guide
Deep Learning for computer vision offline Guide Application icon

Deep Learning for computer vision offline Guide 1.0

38 MB / 0+ Downloads / Rating 5.0 - 1 reviews


See previous versions

Deep Learning for computer vision offline Guide, developed and published by fuzzysystemzone, has released its latest version, 1.0, on 2020-09-07. This app falls under the Books & Reference category on the Google Play Store and has achieved over 1 installs. It currently holds an overall rating of 5.0, based on 1 reviews.

Deep Learning for computer vision offline Guide APK available on this page is compatible with all Android devices that meet the required specifications (Android 2.3+). It can also be installed on PC and Mac using an Android emulator such as Bluestacks, LDPlayer, and others.

Read More

App Screenshot

App Screenshot

App Details

Package name: com.bleeddatascience.deeplearning

Updated: 4 years ago

Developer Name: fuzzysystemzone

Category: Books & Reference

Installation Instructions

This article outlines two straightforward methods for installing Deep Learning for computer vision offline Guide on PC Windows and Mac.

Using BlueStacks

  1. Download the APK/XAPK file from this page.
  2. Install BlueStacks by visiting http://bluestacks.com.
  3. Open the APK/XAPK file by double-clicking it. This action will launch BlueStacks and begin the application's installation. If the APK file does not automatically open with BlueStacks, right-click on it and select 'Open with...', then navigate to BlueStacks. Alternatively, you can drag-and-drop the APK file onto the BlueStacks home screen.
  4. Wait a few seconds for the installation to complete. Once done, the installed app will appear on the BlueStacks home screen. Click its icon to start using the application.

Using LDPlayer

  1. Download and install LDPlayer from https://www.ldplayer.net.
  2. Drag the APK/XAPK file directly into LDPlayer.

If you have any questions, please don't hesitate to contact us.

Previous Versions

Deep Learning for computer vision offline Guide 1.0
2020-09-07 / 38 MB / Android 2.3+

About this app

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

The goal of this app is to introduce deep learning enthusiast to computer vision, starting from basics and then turning to more modern deep learning models. Deep learning for Computer vision app will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.

Main Specific Topic:

✔ What is Deep Learning?.
✔ Image Fundamentals.
✔ Image Classification Basics.
✔ Datasets for Image Classification.
✔ Configuring Your Development Environment
✔ Create First Image Classifier.
✔ Parameterized Learning
✔ Optimization Methods and Regularization.
✔ Neural Network Fundamentals.
✔ Convolutional Neural Networks.
✔ Training Your First CNN .
✔ Saving and Loading Your Models
✔ LeNet: Recognizing Handwritten Digits.
✔ MiniVGGNet: Going Deeper with CNNs
✔ Learning Rate Schedulers.
✔ Spotting Underfitting and Overfitting.
✔ Checkpointing Models.
✔ Visualizing Network Architectures.
✔ Computer Vision Concepts and Practice examples
✔ Out-of-the-box CNNs for Classification.
✔ Python programming core topics.
✔ Data Science Topics Python Code examples.
✔ Machine Learning with Code examples.